Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Biostatistics: Overview01:20

Biostatistics: Overview

715
Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...
715
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

1.4K
Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
1.4K
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

14.1K
When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
14.1K
Overview of Biostatistics in Health Sciences01:19

Overview of Biostatistics in Health Sciences

4.5K
Biostatistics involves the application of statistical techniques to scientific research in health-related fields, including biology and public health. These techniques are essential for designing studies, collecting data, and analyzing it to draw meaningful conclusions. Given the complexity of biological processes, particularly in studies involving human subjects, biostatistical methods are crucial for effectively organizing and interpreting data that might otherwise obscure underlying patterns...
4.5K
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

888
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
888
Statistical Analysis System (SAS)01:14

Statistical Analysis System (SAS)

810
SAS, short for Statistical Analysis System, is a powerful data analysis, management, and visualization tool. Developed by the SAS Institute in the early 1970s, SAS has evolved into a comprehensive software suite used across various industries for statistical analysis, business intelligence, and predictive modeling.
Applications: SAS finds applications in numerous fields, including healthcare for clinical trial analysis, finance for risk assessment, marketing for customer data analysis, and...
810

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Oat-rich low-gluten diet modulates plasma short-chain fatty acids without significant changes in fecal microbiome or inflammatory markers - a randomized clinical trial in people with cardiometabolic risk.

The Journal of nutrition·2026
Same author

Reconstructing community dynamics from limited observations.

Microbiome·2026
Same author

How can biological databases support the new UN mechanism for benefit-sharing from digital sequence information?

Scientific data·2026
Same author

From "synthetic" to defined microbial communities for clearer terminology.

Nature communications·2026
Same author

Skin microbiota variation among Indian monozygotic twins.

PeerJ·2026
Same author

An LC-MS untargeted metabolomic comparison between three blood microsampling devices, whole blood, and plasma.

Metabolomics : Official journal of the Metabolomic Society·2026
Same journal

Cross-Domain Transfer Learning from Peptides to Metabolites Using a Multi-Property Fine-Tuned LLM.

Bioinformatics (Oxford, England)·2026
Same journal

Biomedical Concept Recognition with Error-aware Negative-enhanced Ranking Framework.

Bioinformatics (Oxford, England)·2026
Same journal

TEDLH: Domain HMMs for sensitive detection of remote homologues.

Bioinformatics (Oxford, England)·2026
Same journal

PLNFGL: Joint Estimation of Multi-Condition Gene Networks from Single-cell RNA-seq Data.

Bioinformatics (Oxford, England)·2026
Same journal

MCFST: Spatial domain identification method based on multi-view graph convolutional network and graph fusion network.

Bioinformatics (Oxford, England)·2026
Same journal

SpaBiT: Enhancing Spatial Transcriptomics Resolution via Bidirectional Attention Transformers.

Bioinformatics (Oxford, England)·2026
See all related articles

Related Experiment Video

Updated: Jan 11, 2026

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

2.0K

HoloFoodR: a statistical programming framework for holo-omics data integration workflows.

Tuomas Borman1, Artur Sannikov2, Robert D Finn3

  • 1Department of Computing, University of Turku, Turku 20014, Finland.

Bioinformatics (Oxford, England)
|November 9, 2025
PubMed
Summary
This summary is machine-generated.

Holo-omics integrates host and microbiome data for interaction studies. A new R package, HoloFoodR, bridges data resources and analysis tools, enabling reproducible research workflows in this emerging field.

More Related Videos

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

2.7K
Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis
09:58

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis

Published on: June 27, 2020

3.1K

Related Experiment Videos

Last Updated: Jan 11, 2026

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

2.0K
Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

2.7K
Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis
09:58

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis

Published on: June 27, 2020

3.1K

Area of Science:

  • Microbiome Research
  • Systems Biology
  • Bioinformatics

Background:

  • Holo-omics integrates multi-omic datasets from host organisms and their microbiomes.
  • Curated, open-access holo-omic databases like HoloFood are emerging.
  • A gap exists between holo-omic data resources and analytical frameworks.

Purpose of the Study:

  • To address the challenge of integrating holo-omic data resources with algorithmic frameworks.
  • To facilitate the design of open and reproducible research workflows in holo-omics.

Main Methods:

  • Development of the HoloFoodR R/Bioconductor package.
  • Leveraging statistical programming with curated holo-omic datasets.
  • Utilizing the HoloFood database containing ~10,000 holo-omic profiles for salmon and chicken.

Main Results:

  • The HoloFoodR package provides an algorithmic framework for holo-omic data analysis.
  • Enables the integration of multi-omic datasets for host-microbiome interaction studies.
  • Facilitates reproducible research through open-source code and curated data.

Conclusions:

  • Combining statistical programming advances with curated holo-omic data enables open and reproducible workflows.
  • HoloFoodR enhances the utility of holo-omic databases for scientific inquiry.
  • This work supports the advancement of the emerging field of holo-omics.